Data augmentation constitutes a different class of regularization methods [1]}, [2]}, [3]}, which can also be either data-agnostic or data-dependent. Data augmentation involves training a model with not just the original data, but also with additional data that is properly transformed, and it has led to state-of-the-art results in image recognition [4]}, [5]}. The recently-proposed data-agnostic method, mixup [6]}, trains a model on linear interpolations of a random pair of examples and their corresponding labels, thereby encouraging the model to behave linearly in-between training examples. Both noise injection and mixup have been shown to impose smoothness and increase model robustness to data perturbations [7]}, [8]}, [9]},
which is critical for many safety and sensitive applications [10]}, [11]}.
